This document is the summary of the Introduction to R workshop.
All correspondence related to this document should be addressed to:
Omid Ghasemi (Macquarie University, Sydney, NSW, 2109, AUSTRALIA)
Email: omidreza.ghasemi@hdr.mq.edu.auThe aim of the study is to test if simple arguments are more effective in belief revision than more complex arguments. To that end, we present participants with an imaginary scenario (two alien creatures on a planet) and a theory (one creature is predator and the other one is prey) and we ask them to rate the likelihood truth of the theory based on a simple fact (We adapted this method from Gregg et al.,2017; see the original study here). Then, in a between-subject manipulation, participants will be presented with either 6 simple arguments (Modus Ponens conditionals) or 6 more complex arguments (Modus Tollens conditionals), and they will be asked to rate the likelihood truth of the initial theory on 7 stages.
The first stage is the base rating stage. The next three stages include supportive arguments of the theory and the last three arguments include disproving arguments of the theory. We hypothesized that the group with simple arguments shows better persuasion (as it reflects in higher ratings for the supportive arguments) and better dissuasion (as it reflects in lower ratings for the opposing arguments).
In the last part of the study, participants will be asked to answer several cognitive capacity/style measures including thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales. We hypothesized that cognitive ability, cognitive style, and open-mindedness are positive predictors of persuasion and dissuasion. These associations should be more pronounced for participants in the group with complex arguments because the ability and willingness to engage in deliberative thinking may favor participants to assess the underlying logical structure of those arguments. However, for participants in the simple group, the logical structure of arguments is more evident, so participants with lower ability can still assess the logical status of those arguments.
Thus, our hypotheses for this experiment are as follows:
Participants in the group with simple arguments have higher ratings for supportive arguments (They are more easily persuaded than those in the group with complex arguments).
Participants in the group with simple arguments have lower ratings for opposing arguments (They are more easily dissuaded than those in the group with complex arguments).
There are significant associations between thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales with both persuasion and dissuasion indexes in each group and in the entire sample. The relationship between these measures should be stronger, although not significantly, for participants in the group with complex arguments.
First, we need to design the experiment. For this experiment, we use online platforms for data collection. There are several options such as Gorilla, JSpsych, Qualtrics, psychoJS (pavlovia), etc. Since we do not need any reaction time data, we simply use Qualtrics. For an overview of different lab-based and online platforms, see here.
Next, we need to decide on the number of participants (sample size). For this study, we do not sue power analysis since we cannot access more than 120 participants. However, it is highly suggested calculate sample size using power estimation. You can find some nice tutorials on how to do that here, here, and here.
After we created the experiment and decided on the sample size, the next step is to preresigter the study. However, it would be better to do a pilot with 4 or 5 participants, clean all the data, do the desired analysis, and then pre-register the analysis and those codes. You can find the preregistration form for the current study here.
Finally, we need to restructure our project in a tidy folder with different sub-folders. Having a clean and tidy folder structure can save us! There are different formats of folder structure (for example, see here and here), but for now, we use the following structure:
# load libraries
library(tidyverse)
library(here)
library(janitor)
library(broom)
library(afex)
library(emmeans)
library(knitr)
library(kableExtra)
library(ggsci)
library(patchwork)
library(skimr)
# install.packages("devtools")
# devtools::install_github("easystats/correlation")
library("correlation")
options(scipen=999) # turn off scientific notations
options(contrasts = c('contr.sum','contr.poly')) # set the contrast sum globally
options(knitr.kable.NA = '')
Artwork by Allison Horst: https://github.com/allisonhorst/stats-illustrations
R can be used as a calculator. For mathematical purposes, be careful of the order in which R executes the commands.
10 + 10
## [1] 20
4 ^ 2
## [1] 16
(250 / 500) * 100
## [1] 50
R is a bit flexible with spacing (but no spacing in the name of variables and words)
10+10
## [1] 20
10 + 10
## [1] 20
R can sometimes tell that you’re not finished yet
10 +
How to create a variable? Variable assignment using <- and =. Note that R is case sensitive for everything
pay <- 250
month = 12
pay * month
## [1] 3000
salary <- pay * month
Few points in naming variables and vectors: use short, informative words, keep same method (e.g., not using capital words, use only _ or . ).
Function is a set of statements combined together to perform a specific task. When we use a block of code repeatedly, we can convert it to a function. To write a function, first, you need to define it:
my_multiplier <- function(a,b){
result = a * b
return (result)
}
This code do nothing. To get a result, you need to call it:
my_multiplier (2,4)
## [1] 8
Fortunately, you do not need to write everything from scratch. R has lots of built-in functions that you can use:
round(54.6787)
## [1] 55
round(54.5787, digits = 2)
## [1] 54.58
Use ? before the function name to get some help. For example, ?round. You will see many functions in the rest of the workshop.
function class() is used to show what is the type of a variable.
TRUE, FALSE can be abbreviated as T, F. They has to be capital, ‘true’ is not a logical data:class(TRUE)
## [1] "logical"
class(F)
## [1] "logical"
class(2)
## [1] "numeric"
class(13.46)
## [1] "numeric"
class("ha ha ha ha")
## [1] "character"
class("56.6")
## [1] "character"
class("TRUE")
## [1] "character"
Can we change the type of data in a variable? Yes, you need to use the function as.---()
as.numeric(TRUE)
## [1] 1
as.character(4)
## [1] "4"
as.numeric("4.5")
## [1] 4.5
as.numeric("Hello")
## Warning: NAs introduced by coercion
## [1] NA
Vector: when there are more than one number or letter stored. Use the combine function c() for that.
sale <- c(1, 2, 3,4, 5, 6, 7, 8, 9, 10) # also sale <- c(1:10)
sale <- c(1:10)
sale * sale
## [1] 1 4 9 16 25 36 49 64 81 100
Subsetting a vector:
days <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
days[2]
## [1] "Sunday"
days[-2]
## [1] "Saturday" "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"
days[c(2, 3, 4)]
## [1] "Sunday" "Monday" "Tuesday"
Create a vector named my_vector with numbers from 0 to 1000 in it:
my_vector <- (0:1000)
mean(my_vector)
## [1] 500
median(my_vector)
## [1] 500
min(my_vector)
## [1] 0
range(my_vector)
## [1] 0 1000
class(my_vector)
## [1] "integer"
sum(my_vector)
## [1] 500500
sd(my_vector)
## [1] 289.1081
List: allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other list.
my_list = list(sale, 1, 3, 4:7, "HELLO", "hello", FALSE)
my_list
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4 5 6 7
##
## [[5]]
## [1] "HELLO"
##
## [[6]]
## [1] "hello"
##
## [[7]]
## [1] FALSE
Factor: Factors store the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character. For example, variable gender with “male” and “female” entries:
gender <- c("male", "male", "male", " female", "female", "female")
gender <- factor(gender)
R now treats gender as a nominal (categorical) variable: 1=female, 2=male internally (alphabetically).
summary(gender)
## female female male
## 1 2 3
Question: why when we ran the above function i.e. summary(), it showed three and not two levels of the data? Hint: run ‘gender’.
gender
## [1] male male male female female female
## Levels: female female male
So, be careful of spaces!
Create a gender factor with 30 male and 40 females (Hint: use the rep() function):
gender <- c(rep("male",30), rep("female", 40))
gender <- factor(gender)
gender
## [1] male male male male male male male male male male
## [11] male male male male male male male male male male
## [21] male male male male male male male male male male
## [31] female female female female female female female female female female
## [41] female female female female female female female female female female
## [51] female female female female female female female female female female
## [61] female female female female female female female female female female
## Levels: female male
There are two types of categorical variables: nominal and ordinal. How to create ordered factors (when the variable is nominal and values can be ordered)? We should add two additional arguments to the factor() function: ordered = TRUE, and levels = c("level1", "level2"). For example, we have a vector that shows participants’ education level.
edu<-c(3,2,3,4,1,2,2,3,4)
education<-factor(edu, ordered = TRUE)
levels(education) <- c("Primary school","high school","College","Uni graduated")
education
## [1] College high school College Uni graduated
## [5] Primary school high school high school College
## [9] Uni graduated
## Levels: Primary school < high school < College < Uni graduated
We have a factor with patient and control values. Here, the first level is control and the second level is patient. Change the order of levels, so patient would be the first level:
health_status <- factor(c(rep('patient',5),rep('control',5)))
health_status
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: control patient
health_status_reordered <- factor(health_status, levels = c('patient','control'))
health_status_reordered
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: patient control
Finally, can you relabel both levels to uppercase characters? (Hint: check ?factor)
health_status_relabeled <- factor(health_status, levels = c('patient','control'), labels = c('Patient','Control'))
health_status_relabeled
## [1] Patient Patient Patient Patient Patient Control Control Control
## [9] Control Control
## Levels: Patient Control
Matrices: All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. It can be created using a vector input to the matrix function.
my_matrix = matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, ncol = 3)
my_matrix
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Data frames: (two-dimensional objects) can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type. Let’s create a dataframe:
id <- 1:200
group <- c(rep("Psychotherapy", 100), rep("Medication", 100))
response <- c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5))
my_dataframe <-data.frame(Patient = id,
Treatment = group,
Response = response)
We also could have done the below
my_dataframe <-data.frame(Patient = c(1:200),
Treatment = c(rep("Psychotherapy", 100), rep("Medication", 100)),
Response = c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5)))
In large data sets, the function head() enables you to show the first observations of a data frames. Similarly, the function tail() prints out the last observations in your data set.
head(my_dataframe)
tail(my_dataframe)
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 29.33880 |
| 2 | Psychotherapy | 29.04539 |
| 3 | Psychotherapy | 29.42942 |
| 4 | Psychotherapy | 28.60653 |
| 5 | Psychotherapy | 30.50306 |
| 6 | Psychotherapy | 30.93849 |
| Patient | Treatment | Response | |
|---|---|---|---|
| 195 | 195 | Medication | 27.34764 |
| 196 | 196 | Medication | 21.88196 |
| 197 | 197 | Medication | 24.01917 |
| 198 | 198 | Medication | 15.78429 |
| 199 | 199 | Medication | 30.85827 |
| 200 | 200 | Medication | 17.21666 |
Similar to vectors and matrices, brackets [] are used to selects data from rows and columns in data.frames:
my_dataframe[35, 3]
## [1] 22.91956
How can we get all columns, but only for the first 10 participants?
my_dataframe[1:10, ]
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 29.33880 |
| 2 | Psychotherapy | 29.04539 |
| 3 | Psychotherapy | 29.42942 |
| 4 | Psychotherapy | 28.60653 |
| 5 | Psychotherapy | 30.50306 |
| 6 | Psychotherapy | 30.93849 |
| 7 | Psychotherapy | 37.83544 |
| 8 | Psychotherapy | 29.40139 |
| 9 | Psychotherapy | 17.50681 |
| 10 | Psychotherapy | 34.75695 |
How to get only the Response column for all participants?
my_dataframe[ , 3]
## [1] 29.33880 29.04539 29.42942 28.60653 30.50306 30.93849 37.83544
## [8] 29.40139 17.50681 34.75695 31.35813 33.65639 25.53590 37.57608
## [15] 31.31691 29.54266 29.71047 38.31286 26.21947 32.26382 26.50245
## [22] 34.71294 23.62526 26.69715 34.33098 30.39921 31.50005 33.45303
## [29] 30.24174 36.12268 24.47764 28.24003 27.08510 28.12076 22.91956
## [36] 32.23087 25.11534 29.77199 23.34438 23.95160 31.57061 27.46609
## [43] 29.77943 24.75907 31.12372 35.29385 29.42630 37.33090 27.86225
## [50] 29.15896 25.79285 24.97481 32.73731 38.64154 29.97535 28.00618
## [57] 30.33081 27.61340 22.32286 31.36031 34.81418 24.50591 21.49797
## [64] 28.04902 25.53702 31.17193 26.04975 25.81283 30.14459 38.30252
## [71] 27.39544 33.43284 33.78805 28.97254 23.05480 29.09959 29.82267
## [78] 43.41636 35.61809 37.65561 27.39195 35.85190 37.08226 24.07814
## [85] 34.51841 31.74390 37.08256 32.69440 28.63921 29.28099 24.86621
## [92] 26.47702 30.95217 28.99093 33.91155 28.69188 34.32301 29.84054
## [99] 26.58818 35.38726 42.07179 28.01028 27.10762 20.44694 11.23646
## [106] 23.66979 27.34242 34.30978 14.71041 24.82424 21.72212 28.37345
## [113] 20.92716 23.26870 22.22755 23.98742 28.26148 22.51486 23.42425
## [120] 25.11480 26.36407 19.29037 12.20373 31.95148 22.71679 27.03511
## [127] 33.20864 23.37395 24.46956 28.91407 25.44949 18.59792 23.00491
## [134] 33.33111 19.59739 20.59956 22.06965 21.54432 23.88997 26.51995
## [141] 21.56339 27.95391 29.71531 28.84253 27.68215 29.13739 16.28183
## [148] 24.62314 18.21143 27.83460 27.35662 28.59638 28.35563 27.91839
## [155] 21.39462 29.62328 35.87139 26.14519 29.53364 33.55280 28.26295
## [162] 28.80108 29.21218 25.29247 36.12032 32.80811 24.72078 29.75031
## [169] 24.30425 15.64228 20.24308 23.64338 30.12310 21.45499 32.64240
## [176] 20.13246 21.19054 22.31078 28.06280 21.42484 25.84816 28.11297
## [183] 17.22196 23.83372 22.98554 21.10372 20.20799 20.01787 31.93418
## [190] 29.31837 18.05967 33.30776 23.66329 26.66443 27.34764 21.88196
## [197] 24.01917 15.78429 30.85827 17.21666
Another easier way for selecting particular items is using their names that is more helpful than number of the rows in large data sets:
my_dataframe[ , "Response"]
# OR:
my_dataframe$Response